Workshop to be held in conjunction with the KDD 2020 Conference
August 24, 2020
San Diego, CA A virtual event
"Why 2020 will be the Year of Automated Machine Learning"...this was the title of a recent GigaBit Magazine article. The reason is that "AutoML represents the next stage in ML’s evolution, promising to help non-tech companies access the capabilities they need to quickly and cheaply build ML applications". However, one Harvard Business Review article highlighted the "risks of AutoML" and provided some guidance to avoid those risks. According to Forbes in December of 2018, one of the 5 Artificial Intelligence Trends To Watch Out For In 2019 is the gain in prominence of automated machine learning. The term AutoML is appearing more and more in data science discussions, publications, applications, and systems, as an aid to build better machine learning models. AutoML is being used in autonomous driving applications, sales forecasting and lead prioritization systems, and in many generic systems to generate and optimize machine learning pipelines that can select features, transform data, select the best model type and optimize hyperparameters. The debates continue regarding the level to which data science can and should be automated, the level of machine learning knowledge and expertise needed to build quality models, and the where and when manual intervention is necessary, yet the development and application of approaches and tools to automate repeated tasks continues to increase. The advancement, education, and adoption of data mining and machine learning practices require a transformation of theory to application, and feedback from application to theory. The development of tools to automate data mining efforts fosters this transformation and feedback and also promotes the development of standards and the adoption of these standards. Automated standards enable researchers and practitioners to better communicate, sharing successes and challenges in a more consistent common language. In an age of software as a service and ever-increasing scalability requirements, standards are necessary. Consistent adoption, application, and communication in turn promote research and refinement of the automated strategies and growth of the community. To keep pace with the rapidly increasing volume and rate of data generation, standardization and automating of data mining activities are critical. The challenges that must be discussed relate to the boundaries of automated tasks and individual attention needed for each unique business and data scenario.
The goals of the AutoML workshop are:
· To identify opportunities and challenges for automation in machine learning
· To provide an opportunity for researchers to discuss best practices for automation in machine learning, potentially leading to definition of standards
· To provide a forum for researchers to speak out and debate on different ideas in the area of automation in machine learning
Technical Sponsors: